Virtual Metrology (VM) predicts end-of-batch properties (metrology data) from measurable input data composed of pre-process metrology and fault detection and classi cation (FDC) system outputs. This dissertation aims at moving a step closer to the realization of VM in semiconductor manufacturing by providing solutions to the challenges that present VM technology faces. First, various VM methods are introduced and compared in terms of prediction accuracy using four industrial datasets collected from a plasma etch system at Texas Instruments, Inc.. Kalman lter estimation is employed in a novel way to serve as a VM model for predicting outputs of a static process. Recursive PLS regression (R-PLSR) and Kalman filter show the best prediction results as they update the model whenever new measurements are available. Next, two PLS variants (PLS with EWMA mean update and recursive PLS) are proposed as robust VM algorithms that can predict process outputs fairly accurately in the presence of unexpected process drifts and noise. The obtained results reinforce VM technology by suggesting appropriate prediction methods when unexpected process changes occur. For a successful implementation of VM, the data entering the VM model needs to be free from faults. Fault-free (reconstructed) data are obtained by performing fault detection, fault identi cation, and fault reconstruction. A novel fault detection method based on statistics pattern analysis (SPA) is presented. The SPA method provides better fault detection performance for diff erent types of faults as compared to the MPCA-based methods. Next, three well-known fault identi cation methods present in literature are implemented. An equation that relates the RBC with the SVI is derived. The contribution plot method identi es a smaller number of faults correctly as compared to the RBC and the SVI methods. Fairly good estimates of the fault magnitude are obtained when the faults are identi ed correctly. An approach that combines physical measurements with the VM estimates to develop a more robust approach than using VM alone is presented. EWMA-R2R control is implemented using three well-known sampling methods in order to demonstrate the superior performance of two novel control schemes: B-EWMA R2R control and VM-assisted EWMA-R2R control. A new reliance index, which is attractive from a mathematical and practical point of view, is proposed. The VM-assisted EWMA-R2R control yields the best control results among the control schemes employed in this study. The simulation results demonstrate that VM has the potential to reduce measurement costs signi cantly while promising better process control. / text

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Work-related injuries are a persistent problem in the manufacturing industry. This research focuses on factors involved in the incidence, severity, and effective treatment of work-related injuries in a population of manufacturing workers. Data from a large Midwestern manufacturing facility were obtained with the aims of measuring the association between shift work and injury incidence, measuring the impact of injury reporting lag on injury severity, describing an intervention designed to provide expedited treatment to injured workers, and describing worker and injury characteristics associated with treatment success. Using injury and employment data from the Midwestern manufacturing facility for the years 2011 and 2012, we found that workers on second shift had a marginally significant increase in injury incidence compared to first shift workers. No differences were observed between third shift and first shift workers. Gender and job tenure were also found to be associated with increased injury rates. Job tenure was, in fact, a more significant predictor of injury than age. Using injury data from the years 2011 and 2012, we found that delayed injury reporting had a significant impact on injury severity. As the lag time increased between the date of injury and the injury report date, so too did the odds that the injury would lead to restricted work days. We did not, however, find the same association between reporting lag and lost work days. Injury type was a significant predictor of both restricted and lost days. Job tenure and body part injured were also predictors of lost days. Finally, we collected data from the years 2007-2009 on injured workers treated for musculoskeletal disorders through an intervention designed to reduce treatment lag time. The intervention, delivered by occupational health nurses and physical therapists, provided injured workers with a physical therapy visit within three days of reporting an injury. The intervention was designed to circumvent two barriers to timely care, the delay between the injury report date and the first occupational health physician visit, and the delay between the first physician visit and the first physical therapy visit. The most significant predictor of program discharge success was patient age. Older workers tended to have lower odds of being discharged to their baseline work duties compared to young workers. Overall, nearly two-thirds of the injured workers referred to the program were successfully discharged, regardless of gender, body part injured, cause of injury, or nature of injury. This project addresses the important issue of injuries in the manufacturing industry. We provide evidence on the factors associated with injury incidence and injury severity among workers in a large Midwestern manufacturing facility. We also show that workplace injury treatment interventions directed by occupational health nurses and physical therapists can be very effective in returning injured workers to their regular job duties. Our evidence suggests that future research and injury prevention efforts should focus on shift workers, low tenured workers, reducing delayed injury reporting, and reducing delayed injury treatment.